Zobrazeno 1 - 10
of 354
pro vyhledávání: '"Ngo Dat"'
Autor:
Lam, Phat, Pham, Lam, Nguyen, Truong, Ngo, Dat, Pham, Thinh, Nguyen, Tin, Nguyen, Loi Khanh, Schindler, Alexander
Existing speaker diarization systems typically rely on large amounts of manually annotated data, which is labor-intensive and difficult to obtain, especially in real-world scenarios. Additionally, language-specific constraints in these systems signif
Externí odkaz:
http://arxiv.org/abs/2407.01963
In this paper, we propose a deep learning based model for Acoustic Anomaly Detection of Machines, the task for detecting abnormal machines by analysing the machine sound. By conducting extensive experiments, we indicate that multiple techniques of ps
Externí odkaz:
http://arxiv.org/abs/2403.00379
This paper presents a deep learning system applied for detecting anomalies from respiratory sound recordings. Our system initially performs audio feature extraction using Continuous Wavelet transformation. This transformation converts the respiratory
Externí odkaz:
http://arxiv.org/abs/2306.14929
In this technical report, a low-complexity deep learning system for acoustic scene classification (ASC) is presented. The proposed system comprises two main phases: (Phase I) Training a teacher network; and (Phase II) training a student network using
Externí odkaz:
http://arxiv.org/abs/2305.09463
In this paper, we present a deep learning based multimodal system for classifying daily life videos. To train the system, we propose a two-phase training strategy. In the first training phase (Phase I), we extract the audio and visual (image) data fr
Externí odkaz:
http://arxiv.org/abs/2305.01476
This paper presents a deep learning system applied for detecting anomalies from respiratory sound recordings. Initially, our system begins with audio feature extraction using Gammatone and Continuous Wavelet transformation. This step aims to transfor
Externí odkaz:
http://arxiv.org/abs/2303.04104
Autor:
Pham, Lam, Le, Cam, Ngo, Dat, Nguyen, Anh, Lampert, Jasmin, Schindler, Alexander, McLoughlin, Ian
In this paper, we present a high-performance and light-weight deep learning model for Remote Sensing Image Classification (RSIC), the task of identifying the aerial scene of a remote sensing image. To this end, we first valuate various benchmark conv
Externí odkaz:
http://arxiv.org/abs/2302.13028
Autor:
Han, Siyeon1 (AUTHOR) 1923602@donga.ac.kr, Ngo, Dat2 (AUTHOR) datngo@ut.ac.kr, Choi, Yeonggyu2 (AUTHOR) ygchoi@ut.ac.kr, Kang, Bongsoon1 (AUTHOR) bongsoon@dau.ac.kr
Publikováno v:
Remote Sensing. Oct2024, Vol. 16 Issue 19, p3641. 19p.
Autor:
Ngo, Dat1 (AUTHOR) datngo@ut.ac.kr, Han, Siyeon2 (AUTHOR) 1923602@donga.ac.kr, Kang, Bongsoon2 (AUTHOR) bongsoon@dau.ac.kr
Publikováno v:
Symmetry (20738994). Sep2024, Vol. 16 Issue 9, p1138. 15p.
Autor:
Ngo, Dat T., Nguyen, Thao T. B., Nguyen, Hieu T., Nguyen, Dung B., Nguyen, Ha Q., Pham, Hieu H.
The rapid development in representation learning techniques such as deep neural networks and the availability of large-scale, well-annotated medical imaging datasets have to a rapid increase in the use of supervised machine learning in the 3D medical
Externí odkaz:
http://arxiv.org/abs/2208.03403